Abstract
Ranking account influence constitutes an important challenge in social media analysis. Until recently, influence ranking relied solely on the structural properties of the underlying social graph, in particular on connectivity patterns. Currently, there has been a notable shift to the next logical step where network functionality is taken into account, as online social media such as Reddit, Instagram, and Twitter are renowned primarily for their functionality. However, contrary to structural rankings, functional ones are bound to be network-specific since each social platform offers unique interaction possibilities. This article examines seven first-order influence metrics for Twitter, defines a strategy for deriving their higher-order counterparts, and outlines a probabilistic evaluation framework. Experiments with a Twitter subgraph with ground truth influential accounts indicate that a single metric combining structural and functional features outperforms the rest in said framework.
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Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the 4th ACM WSDM. ACM, pp 65–74
Benzi M, Boito P (2010) Quadrature rule-based bounds for functions of adjacency matrices. Linear Algebra Appl 433(3):637–652
Benzi M, Klymko C (2015) On the limiting behavior of parameter-dependent network centrality measures. SIAM J Matrix Anal Appl 36(2):686–706
Bertot JC, Jaeger PT, Grimes JM (2010) Using ICTs to create a culture of transparency: e-government and social media as openness and anti-corruption tools for societies. Gov Inf Q 27(3):264–271
Bi Z, Faloutsos C, Korn F (2001) The DGX distribution for mining massive, skewed data. In: Proceedings of the seventh ACM SIGKDD. ACM, pp 17–26
Bickmore T, Cassell J (2001) Relational agents: a model and implementation of building user trust. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 396–403
Blackmore S (2000) The meme machine. Oxford University Press, Oxford
Bodnar T, Tucker C, Hopkinson K, Bilén SG (2014) Increasing the veracity of event detection on social media networks through user trust modeling. In: 2014 IEEE international conference on big data. IEEE, pp 636–643
Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92:1170–1182
Bouguessa M, Dumoulin B, Wang S (2008) Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, KDD ’08, pp 866–874
Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in twitter: the million follower fallacy. In: ICWSM’10: Proceedings of international AAAI conference on weblogs and social media
Dawkins R (2006) The selfish gene, 3rd edn. Oxford University Press, Oxford
Drakopoulos G (2016) Tensor fusion of social structural and functional analytics over Neo4j. In: Proceedings of the 6th international conference of information, intelligence, systems, and applications. IEEE, IISA 2016
Drakopoulos G, Kanavos A (2016) Tensor-based document retrieval over Neo4j with an application to PubMed mining. In: Proceedings of the 6th international conference of information, intelligence, systems, and applications. IEEE, IISA 2016
Drakopoulos G, Megalooikonomou V (2016) Regularizing large biosignals with finite differences. In: Proceedings of the 6th international conference of information, intelligence, systems, and applications. IEEE, IISA 2016
Drakopoulos G, Baroutiadi A, Megalooikonomou V (2015) Higher order graph centrality measures for Neo4j. In: Proceedings of the 6th international conference of information, intelligence, systems, and applications. IEEE, IISA 2015
Drakopoulos G, Kanavos A, Makris C, Megalooikonomou V (2016a) Finding fuzzy communities in Neo4j. In: Howlett RJ, Jain LC (eds) Smart innovation, systems, and technologies. Springer, Berlin
Drakopoulos G, Kanavos A, Tsakalidis A (2016b) Evaluating Twitter influence ranking with system theory. In: Proceedings of the 12th international conference on web information systems and technologies, WEBIST 2016
Estrada E, Higham DJ (2010) Network properties revealed through matrix functions. SIAM Rev 52(4):696–714
Fiedler M (1973) Algebraic connectivity of graphs. Czechoslov Math J 23(2):298–305
Gao Y (2005) Factors influencing user trust in online games. Electron Libr 23(5):533–538
Golbeck J (2009) Trust and nuanced profile similarity in online social networks. ACM Trans Web 3(4):12
Kafeza E, Kanavos A, Makris C, Chiu D (2013) Identifying personality-based communities in social networks. In: Legal and social aspects in web modeling (Keynote Speech) in conjunction with the international conference on conceptual modeling (ER), LSAWM
Kafeza E, Kanavos A, Makris C, Vikatos P (2014) T-PICE: Twitter personality-based influential communities extraction system. In: IEEE International Congress on Big Data, pp 212–219
Kanavos A, Perikos I, Vikatos P, Hatzilygeroudis I, Makris C, Tsakalidis A (2014a) Conversation emotional modeling in social networks. In: 26th IEEE international conference on tools with artificial intelligence. ICTAI, pp 478–484
Kanavos A, Perikos I, Vikatos P, Hatzilygeroudis I, Makris C, Tsakalidis A (2014b) Modeling retweet diffusion using emotional content. In: Artificial intelligence applications and innovations. AIAI, pp 101–110
Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43
Leskovec J (2011) Social media analytics: tracking, modeling and predicting the flow of information through networks. In: Proceedings of WWW 2011. ACM, pp 277–278
Leskovec J, Huttenlocher D, Kleinberg J (2010) Signed networks in social media. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1361–1370
Leskovec J, Rajamaran A, Ullman JD (2014) Mining of massive datasets, 2nd edn. Cambridge University Press, Cambridge
Li J, Shiu WC, Chang A (2009) On the Laplacian Estrada index of a graph. Appl Anal Discr Math 3:147–156
Lotan G, Graeff E, Ananny M, Gaffney D, Pearce I et al (2011) The Arab Spring-the revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. Int J Commun 5:31
Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of ICDM 2010. ACM, pp 135–146
Manicas PT (1991) History and philosophy of social science. PhilPapers
Mehta R, Mehta D, Chheda D, Shah C, Chawan PM (2012) Sentiment analysis and influence tracking using twitter. Int J Adv Res Comput Sci Electron Eng 1(2):73–79
Newman ME (2005) A measure of betweenness centrality based on random walks. Soc Netw 27(1):39–54
Okamoto K, Chen W, Li XY (2008) Ranking of closeness centrality for large-scale social networks. In: International workshop on frontiers in algorithmics. Springer, pp 186–195
Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the Wweb
Pal A, Counts S (2011) Identifying topical authorities in microblogs. In: Proceedings of the Fourth ACM international conference on web search and data mining. ACM, WSDM ’11, pp 45–54
Panzarino O (2014) Learning cypher. PACKT publishing, Birmingham
Razis G, Anagnostopoulos I (2014) InfluenceTracker: rating the impact of a twitter account. In: Proceedings of AIAI, pp 184–195
Rivest RL, Vuillemin J (1976) On recognizing graph properties from adjacency matrices. Theor Comput Sci 3(3):371–384
Robinson I, Webber J, Eifrem E (2013) Graph databases. O’Reilly, Sebastopol
Rogers EM, Beal GM (1957) The importance of personal influence in the adoption of technological change. Soc F 36:329
Russell MA (2013) Mining the social web: analyzing data from Facebook, Twitter, LinkedIn, and other social media sites, 2nd edn. O’Reilly, Sebastopol
Smith AN, Fischer E, Yongjian C (2012) How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? J Interact Mark 26(2):102–113
Tong H, Prakash BA, Eliassi-Rad T, Faloutsos M, Faloutsos C (2012) Gelling and melting large graphs by edge manipulation. In: Proceedings of the 21st CIKM. ACM, pp 245–254
TunkRank (2015) http://thenoisychannel.com/2009/01/13/a-twitter-analog-to-pagerank
Turner JC (1991) Social influence. Thomson Brooks/Cole Publishing Co, Pacific Grove
Weng J, Lim EP, Jiang J, He Q (2010) TwitterRank: Finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM international conference on web search and data mining. ACM, pp 261–270
Zamparas V, Kanavos A, Makris C (2015) Real time analytics for measuring user influence on twitter. In: Proceedings on the 27th international conference on tools with artificial intelligence. IEEE, pp 591–597
Zimbardo PG, Leippe MR (1991) The psychology of attitude change and social influence. McGraw-Hill Book Company, New York
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Drakopoulos, G., Kanavos, A., Mylonas, P. et al. Defining and evaluating Twitter influence metrics: a higher-order approach in Neo4j. Soc. Netw. Anal. Min. 7, 52 (2017). https://doi.org/10.1007/s13278-017-0467-9
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DOI: https://doi.org/10.1007/s13278-017-0467-9
Keywords
- Humanistic data
- Higher-order data
- Higher-order moments
- Influence metrics
- Structural metrics
- Functional metrics
- Neo4j